1 Materials

1.1 Data

dir.create("data/steinbock/raw", recursive = TRUE)
download.file("https://zenodo.org/record/5949116/files/panel.csv",
              "data/steinbock/raw/panel.csv")
download.file("https://zenodo.org/record/5949116/files/Patient1.zip",
              "data/steinbock/raw/Patient1.zip")
download.file("https://zenodo.org/record/5949116/files/Patient2.zip",
              "data/steinbock/raw/Patient2.zip")
download.file("https://zenodo.org/record/5949116/files/Patient3.zip",
              "data/steinbock/raw/Patient3.zip")
download.file("https://zenodo.org/record/5949116/files/Patient4.zip",
              "data/steinbock/raw/Patient4.zip")
download.file("https://zenodo.org/record/5949116/files/compensation.zip",
              "data/compensation.zip")
unzip("data/compensation.zip", exdir="data", overwrite=TRUE)
unlink("data/compensation.zip")
download.file("https://zenodo.org/record/5949116/files/sample_metadata.xlsx", 
         destfile = "data/sample_metadata.xlsx")
download.file("https://zenodo.org/record/6810879/files/gated_cells.zip",
              "data/gated_cells.zip")
unzip("data/gated_cells.zip", exdir="data", overwrite=TRUE)
unlink("data/gated_cells.zip")

2 Equipment setup

2.1 Installation instructions

if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")

devtools::install_github(c("BodenmillerGroup/imcRtools", 
                           "BodenmillerGroup/cytomapper"))


if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")

BiocManager::install(c("pheatmap", "viridis",
                       "zoo", "BiocManager", "devtools", "tiff",
                       "distill", "openxlsx", "ggrepel", "patchwork", "mclust",
                       "RColorBrewer", "uwot", "Rtsne", "harmony", "Seurat", 
                       "SeuratObject", "cowplot", "kohonen", "caret", 
                       "randomForest", "ggridges", "cowplot", "gridGraphics",
                       "scales", "tiff", "CATALYST", "scuttle", "scater", 
                       "dittoSeq", "tidyverse", "batchelor", 
                       "bluster","scran", "lisaClust", "spicyR"))

3 Procedure

3.1 Preprocessing

steinbock preprocess imc panel --namecol Clean_Target

The step took 0 minutes.

steinbock preprocess imc images --hpf 50

The step took 0 minutes.

steinbock segment deepcell --minmax

The step took 0 minutes.

steinbock measure intensities

The step took 0 minutes.

steinbock measure regionprops

The step took 0 minutes.

steinbock measure neighbors --type expansion --dmax 4

The step took 0 minutes.

3.2 Reading in data

library(imcRtools)
## Warning: package 'imcRtools' was built under R version 4.2.1
## Warning: package 'SpatialExperiment' was built under R version 4.2.1
## Warning: package 'IRanges' was built under R version 4.2.1
## Warning: package 'GenomeInfoDb' was built under R version 4.2.1
spe <- read_steinbock("data/steinbock/")

The step took 0.54 minutes.

library(openxlsx)
library(tidyverse)
colnames(spe) <- paste0(spe$sample_id, "_", spe$ObjectNumber)

meta <- read.xlsx("data/sample_metadata.xlsx")
spe$patient_id <- as.vector(str_extract_all(spe$sample_id, "Patient[1-4]", 
   simplify = TRUE))
spe$ROI <- as.vector(str_extract_all(spe$sample_id, "00[1-8]",  
   simplify = TRUE))
spe$indication <- meta$Indication[match(spe$patient_id, meta$Sample.ID)]

rowData(spe)$use_channel <- !grepl("DNA|Histone", rownames(spe))

assay(spe, "exprs") <- asinh(counts(spe)/1)

The step took 0.02 minutes.

library(cytomapper)
## Warning: package 'cytomapper' was built under R version 4.2.1
images <- loadImages("data/steinbock/img/")
channelNames(images) <- rownames(spe)

The step took 0.17 minutes.

masks <- loadImages("data/steinbock/masks/", as.is = TRUE)
## All files in the provided location will be read in.

The step took 0 minutes.

patient_id <- str_extract_all(names(images), "Patient[1-4]", simplify = TRUE)
indication <- meta$Indication[match(patient_id, meta$Sample.ID)] 

mcols(images) <- mcols(masks) <- DataFrame(sample_id = names(images),
                                           patient_id = patient_id,
                                           indication = indication)

The step took 0 minutes.

cytomapper_sce <- measureObjects(masks, image = images, img_id = "sample_id")

The step took 6.2 minutes.

3.3 Spillover correction

sce <- readSCEfromTXT("data/compensation/")
## Spotted channels:  Y89, In113, In115, Pr141, Nd142, Nd143, Nd144, Nd145, Nd146, Sm147, Nd148, Sm149, Nd150, Eu151, Sm152, Eu153, Sm154, Gd155, Gd156, Gd158, Tb159, Gd160, Dy161, Dy162, Dy163, Dy164, Ho165, Er166, Er167, Er168, Tm169, Er170, Yb171, Yb172, Yb173, Yb174, Lu175, Yb176
## Acquired channels:  Ar80, Y89, In113, In115, Xe131, Xe134, Ba136, La138, Pr141, Nd142, Nd143, Nd144, Nd145, Nd146, Sm147, Nd148, Sm149, Nd150, Eu151, Sm152, Eu153, Sm154, Gd155, Gd156, Gd158, Tb159, Gd160, Dy161, Dy162, Dy163, Dy164, Ho165, Er166, Er167, Er168, Tm169, Er170, Yb171, Yb172, Yb173, Yb174, Lu175, Yb176, Ir191, Ir193, Pt196, Pb206
## Channels spotted but not acquired:  
## Channels acquired but not spotted:  Ar80, Xe131, Xe134, Ba136, La138, Ir191, Ir193, Pt196, Pb206
assay(sce, "exprs") <- asinh(counts(sce)/5)

The step took 0.05 minutes.

plotSpotHeatmap(sce)

sce2 <- binAcrossPixels(sce, bin_size = 10)

The step took 0.21 minutes.

library(CATALYST)

bc_key <- as.numeric(unique(sce$sample_mass))
bc_key <- bc_key[order(bc_key)]

sce <- assignPrelim(sce, bc_key = bc_key)
## Debarcoding data...
##  o ordering
##  o classifying events
## Normalizing...
## Computing deltas...
sce <- estCutoffs(sce)
sce <- applyCutoffs(sce)

sce <- filterPixels(sce, minevents = 40, correct_pixels = TRUE)

The step took 0.15 minutes.

sce <- computeSpillmat(sce)
sm <- metadata(sce)$spillover_matrix

The step took 0.03 minutes.

library(dittoSeq)
library(patchwork)
    
rowData(spe)$channel_name <- paste0(rowData(spe)$channel, "Di")

isotope_list <- CATALYST::isotope_list
isotope_list$Ar <- 80

spe <- compCytof(spe, sm, 
                 transform = TRUE, cofactor = 1,
                 isotope_list = isotope_list, 
                 overwrite = FALSE)

before <- dittoScatterPlot(spe, x.var = "Ecad", y.var = "CD303",
                           assay.x = "exprs", assay.y = "exprs") +
    ggtitle("Before compensation")

after <- dittoScatterPlot(spe, x.var = "Ecad", y.var = "CD303",
                          assay.x = "compexprs", assay.y = "compexprs") +
    ggtitle("After compensation")

before + after

assay(spe, "counts") <- assay(spe, "compcounts") 
assay(spe, "exprs") <- assay(spe, "compexprs") 
assay(spe, "compcounts") <- assay(spe, "compexprs") <- NULL
channelNames(images) <- rowData(spe)$channel_name
adapted_sm <- adaptSpillmat(sm, paste0(rowData(spe)$channel, "Di"), 
                            isotope_list = isotope_list)
## Compensation is likely to be inaccurate.
## Spill values for the following interactions
## have not been estimated:
## Ir191Di -> Ir193Di
## Ir193Di -> Ir191Di
images_comp <- compImage(images, adapted_sm)

plotPixels(images[5], colour_by = "Yb173Di", 
           image_title = list(text = "Yb173 (Ecad) - before", 
                       position = "topleft"), 
           legend = NULL, bcg = list(Yb173Di = c(0, 4, 1)))

plotPixels(images[5], colour_by = "Yb174Di", 
           image_title = list(text = "Yb174 (CD303) - before", 
                              position = "topleft"), 
           legend = NULL, bcg = list(Yb174Di = c(0, 4, 1)))

plotPixels(images_comp[5], colour_by = "Yb173Di",
           image_title = list(text = "Yb173 (Ecad) - after", 
                              position = "topleft"), 
           legend = NULL, bcg = list(Yb173Di = c(0, 4, 1)))

plotPixels(images_comp[5], colour_by = "Yb174Di", 
           image_title = list(text = "Yb174 (CD303) - after", 
                              position = "topleft"),
           legend = NULL, bcg = list(Yb174Di = c(0, 4, 1)))

channelNames(images_comp) <- rownames(spe)

The step took 7.81 minutes.

3.4 Quality control

set.seed(20220118)
img_ids <- sample(seq_len(length(images_comp)), 3)

cur_images <- images_comp[img_ids]
cur_images <- normalize(cur_images, separateImages = TRUE)
cur_images <- normalize(cur_images, inputRange = c(0, 0.2))

plotPixels(cur_images,
           mask = masks[img_ids],
           img_id = "sample_id",
           missing_colour = "white",
           colour_by = c("CD163", "CD20", "CD3", "Ecad", "DNA1"),
           colour = list(CD163 = c("black", "yellow"),
                         CD20 = c("black", "red"),
                         CD3 = c("black", "green"),
                         Ecad = c("black", "cyan"),
                         DNA1 = c("black", "blue")),
           image_title = NULL,
           legend = list(colour_by.title.cex = 0.9,
                         colour_by.labels.cex = 0.9))

colData(spe) %>%
    as.data.frame() %>%
    group_by(sample_id) %>%
    ggplot() +
        geom_boxplot(aes(sample_id, area)) +
        theme_minimal(base_size = 15) + 
        theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 8)) +
        ylab("Cell area") + xlab("")

spe <- spe[,spe$area >= 5]
multi_dittoPlot(spe, vars = c("HLADR", "CD3", "Ecad", "PDGFRb"),
               group.by = "patient_id", plots = c("ridgeplot"), 
               assay = "exprs")
## Picking joint bandwidth of 0.163
## Picking joint bandwidth of 0.0992
## Picking joint bandwidth of 0.151
## Picking joint bandwidth of 0.0895

library(scater)
## Loading required package: scuttle
## Warning: package 'scuttle' was built under R version 4.2.1
set.seed(220225)
spe <- runUMAP(spe, subset_row = rowData(spe)$use_channel, 
 exprs_values = "exprs") 

dittoDimPlot(spe, var = "patient_id", 
     reduction.use = "UMAP", size = 0.2)  +
    ggtitle("Patient ID on UMAP")

The step took 0.56 minutes.

library(batchelor)
set.seed(220228)
out <- fastMNN(spe, batch = spe$patient_id,
               auto.merge = TRUE,
               subset.row = rowData(spe)$use_channel,
               assay.type = "exprs")
## Warning in check_numbers(k = k, nu = nu, nv = nv, limit = min(dim(x)) - : more
## singular values/vectors requested than available
## Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth =
## TRUE, : You're computing too large a percentage of total singular values, use a
## standard svd instead.
reducedDim(spe, "fastMNN") <- reducedDim(out, "corrected")

spe <- runUMAP(spe, dimred= "fastMNN", name = "UMAP_mnnCorrected")

dittoDimPlot(spe, var = "patient_id", 
                   reduction.use = "UMAP_mnnCorrected", size = 0.2) + 
    ggtitle("Patient ID on UMAP after correction")

The step took 2.01 minutes.

3.5 Cell phenotyping

library(bluster)
library(BiocParallel)

mat <- reducedDim(spe, "fastMNN")

set.seed(220825)
combinations <- clusterSweep(mat, BLUSPARAM=SNNGraphParam(),
                             k=c(10L, 20L), 
                             type = c("rank", "jaccard"), 
                             cluster.fun = "louvain")

sil <- vapply(as.list(combinations$clusters), 
              function(x) mean(approxSilhouette(mat, x)$width), 0)

ggplot(data.frame(method = names(sil),
                  sil = sil)) +
    geom_point(aes(method, sil)) +
    theme_classic(base_size = 15) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    xlab("Cluster parameter combination") +
    ylab("Average silhouette width")

The step took 3.06 minutes.

library(scran)

clusters <- clusterCells(spe[rowData(spe)$use_channel,], 
                         use.dimred = "fastMNN", 
                         BLUSPARAM = SNNGraphParam(k=20, 
                                                cluster.fun = "louvain",
                                                type = "rank",
                                                BPPARAM = bpparam()))
spe$nn_clusters <- clusters

The step took 0.78 minutes.

if (interactive()){
    cytomapperShiny(object = spe, mask = masks, image = images_comp, 
                cell_id = "ObjectNumber", img_id = "sample_id")   
}
library(SingleCellExperiment)
label_files <- list.files("data/gated_cells", 
                          full.names = TRUE, pattern = ".rds$")

spes <- lapply(label_files, readRDS)

concat_spe <- do.call("cbind", spes)
## 'sample_id's are duplicated across 'SpatialExperiment' objects to cbind; appending sample indices.
cur_tab <- unclass(table(colnames(concat_spe), 
                         concat_spe$cytomapper_CellLabel))
cur_labels <- rep("doublets", nrow(cur_tab))
names(cur_labels) <- rownames(cur_tab)

# Single assignments
single_index <- rowSums(cur_tab) == 1
cur_labels[single_index] <- colnames(cur_tab)[
apply(cur_tab[single_index,], 1, which.max)]

# Double assignment within the tumor
double_index <- rowSums(cur_tab) == 2 & cur_tab[,"Tumor"] == 1
no_tumor <- cur_tab[,colnames(cur_tab) != "Tumor"]
cur_labels[double_index] <- colnames(no_tumor)[
apply(no_tumor[double_index,], 1, which.max)]

# Remove doublets
cur_labels <- cur_labels[cur_labels != "doublets"]

# Transfer labels to SPE object
spe_labels <- rep("unlabeled", ncol(spe))
names(spe_labels) <- colnames(spe)
spe_labels[names(cur_labels)] <- cur_labels
spe$cell_labels <- spe_labels
library(caret)

# Split between labeled and unlabeled cells
lab_spe <- spe[,spe$cell_labels != "unlabeled"]
unlab_spe <- spe[,spe$cell_labels == "unlabeled"]

# Randomly split into train and test data
set.seed(220510)
trainIndex <- createDataPartition(factor(lab_spe$cell_labels), p = 0.75)
train_spe <- lab_spe[,trainIndex$Resample1]
test_spe <- lab_spe[,-trainIndex$Resample1]

fitControl <- trainControl(method = "cv",
                           number = 5)

cur_mat <- t(assay(train_spe, "exprs")[rowData(train_spe)$use_channel,])

rffit <- train(x = cur_mat, 
               y = factor(train_spe$cell_labels),
               method = "rf", ntree = 1000,
               tuneLength = 5,
               trControl = fitControl)

The step took 9.08 minutes.

cur_mat <- t(assay(test_spe, "exprs")[rowData(test_spe)$use_channel,])

cur_pred <- predict(rffit, 
                    newdata = cur_mat)

confusionMatrix(data = cur_pred, 
                      reference = factor(test_spe$cell_labels), 
                      mode = "everything")
## Confusion Matrix and Statistics
## 
##              Reference
## Prediction    Bcell BnTcell  CD4  CD8 Myeloid Neutrophil Plasma_cell Stroma
##   Bcell         199       2    0    0       0          0           3      0
##   BnTcell         1     395    0    0       0          0           3      0
##   CD4             0       0  168    0       1          0           3      1
##   CD8             0       1    0  194       0          0           3      0
##   Myeloid         0       0    1    2     423          0           0      0
##   Neutrophil      0       0    0    0       0         32           0      0
##   Plasma_cell     0       0    2    0       0          0         156      0
##   Stroma          0       0    0    0       0          0           0    117
##   Treg            0       0    0    0       0          0           0      0
##   Tumor           1       2    1    4       0          1           0      0
##              Reference
## Prediction    Treg Tumor
##   Bcell          0     1
##   BnTcell        0     1
##   CD4            0     1
##   CD8            0     2
##   Myeloid        1     0
##   Neutrophil     0     3
##   Plasma_cell    0     1
##   Stroma         0     0
##   Treg          92     2
##   Tumor          0  1448
## 
## Overall Statistics
##                                          
##                Accuracy : 0.9865         
##                  95% CI : (0.982, 0.9902)
##     No Information Rate : 0.4465         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.9821         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: Bcell Class: BnTcell Class: CD4 Class: CD8
## Sensitivity               0.99005         0.9875    0.97674    0.97000
## Specificity               0.99804         0.9983    0.99806    0.99804
## Pos Pred Value            0.97073         0.9875    0.96552    0.97000
## Neg Pred Value            0.99935         0.9983    0.99871    0.99804
## Precision                 0.97073         0.9875    0.96552    0.97000
## Recall                    0.99005         0.9875    0.97674    0.97000
## F1                        0.98030         0.9875    0.97110    0.97000
## Prevalence                0.06151         0.1224    0.05263    0.06120
## Detection Rate            0.06089         0.1209    0.05141    0.05936
## Detection Prevalence      0.06273         0.1224    0.05324    0.06120
## Balanced Accuracy         0.99405         0.9929    0.98740    0.98402
##                      Class: Myeloid Class: Neutrophil Class: Plasma_cell
## Sensitivity                  0.9976          0.969697            0.92857
## Specificity                  0.9986          0.999073            0.99903
## Pos Pred Value               0.9906          0.914286            0.98113
## Neg Pred Value               0.9996          0.999691            0.99614
## Precision                    0.9906          0.914286            0.98113
## Recall                       0.9976          0.969697            0.92857
## F1                           0.9941          0.941176            0.95413
## Prevalence                   0.1297          0.010098            0.05141
## Detection Rate               0.1294          0.009792            0.04774
## Detection Prevalence         0.1307          0.010710            0.04865
## Balanced Accuracy            0.9981          0.984385            0.96380
##                      Class: Stroma Class: Treg Class: Tumor
## Sensitivity                0.99153     0.98925       0.9925
## Specificity                1.00000     0.99937       0.9950
## Pos Pred Value             1.00000     0.97872       0.9938
## Neg Pred Value             0.99968     0.99968       0.9939
## Precision                  1.00000     0.97872       0.9938
## Recall                     0.99153     0.98925       0.9925
## F1                         0.99574     0.98396       0.9931
## Prevalence                 0.03611     0.02846       0.4465
## Detection Rate             0.03580     0.02815       0.4431
## Detection Prevalence       0.03580     0.02876       0.4458
## Balanced Accuracy          0.99576     0.99431       0.9937
cur_mat <- t(assay(unlab_spe, "exprs")[rowData(unlab_spe)$use_channel,])

cell_class <- as.character(predict.train(rffit, 
                                         newdata = cur_mat, 
                                         type = "raw"))
names(cell_class) <- rownames(cur_mat)

cell_prob <- predict.train(rffit, 
                           newdata = cur_mat, 
                           type = "prob")
cell_class[rowMax(as.matrix(cell_prob)) < 0.4] <- "undefined"

cell_labels <- spe$cell_labels
cell_labels[colnames(unlab_spe)] <- cell_class
spe$celltype <- cell_labels
p1 <- dittoDimPlot(spe, var = "celltype", 
             reduction.use = "UMAP_mnnCorrected", size = 0.2,
             do.label = TRUE) +
  ggtitle("Cell types on UMAP, integrated cells")

p2 <- dittoDimPlot(spe, var = "nn_clusters", 
             reduction.use = "UMAP_mnnCorrected", size = 0.2,
             do.label = TRUE) +
  ggtitle("Clusters on UMAP, integrated cells")

p1 + p2

library(scuttle)
library(viridis)
## Loading required package: viridisLite
celltype_mean <- aggregateAcrossCells(as(spe, "SingleCellExperiment"),  
                     ids = spe$celltype, 
                     statistics = "mean",
                     use.assay.type = "exprs",
                     subset_row = rowData(spe)$use_channel)

dittoHeatmap(celltype_mean,
             assay = "exprs", cluster_cols = TRUE, 
             scale = "none",
             heatmap.colors = viridis(100),
             annot.by = c("celltype","ncells"))

cluster_mean <- aggregateAcrossCells(as(spe, "SingleCellExperiment"),  
                     ids = spe$nn_clusters, 
                     statistics = "mean",
                     use.assay.type = "exprs",
                     subset_row = rowData(spe)$use_channel)

dittoHeatmap(cluster_mean,
             assay = "exprs", cluster_cols = TRUE, 
             scale = "none",
             heatmap.colors = viridis(100),
             annot.by = c("nn_clusters","ncells"))

3.6 Spatial analysis

library(igraph)

# Spatial community detection - tumor
tumor_spe <- spe[,spe$celltype == "Tumor"]

gr <- graph_from_data_frame(as.data.frame(colPair(tumor_spe, "neighborhood")), 
                            directed = FALSE, 
                            vertices = data.frame(index = seq_len(ncol(tumor_spe))))

cl_comm <- cluster_louvain(gr)
comm_tumor <- paste0("Tumor_", membership(cl_comm))
comm_tumor[membership(cl_comm) %in% which(sizes(cl_comm) < 10)] <- NA
names(comm_tumor) <- colnames(tumor_spe)

# Spatial community detection - non-tumor
stroma_spe <- spe[,spe$celltype != "Tumor"]

gr <- graph_from_data_frame(as.data.frame(colPair(stroma_spe, "neighborhood")), 
                            directed = FALSE, 
                            vertices = data.frame(index = seq_len(ncol(stroma_spe))))

cl_comm <- cluster_louvain(gr)
comm_stroma <- paste0("Stroma_", membership(cl_comm))
comm_stroma[membership(cl_comm) %in% which(sizes(cl_comm) < 10)] <- NA
names(comm_stroma) <- colnames(stroma_spe)

comm <- c(comm_tumor, comm_stroma)

spe$spatial_community <- comm[colnames(spe)]

The step took 0.02 minutes.

plotSpatial(spe[,spe$celltype == "Tumor"], 
            node_color_by = "spatial_community", 
            img_id = "sample_id", 
            node_size_fix = 0.5) +
    theme(legend.position = "none") +
    scale_color_manual(values = rev(colors()))

library(pheatmap)
spe <- buildSpatialGraph(spe, img_id = "sample_id", type = "knn", k = 20)


spe <- aggregateNeighbors(spe, colPairName = "knn_interaction_graph", 
                          aggregate_by = "metadata", count_by = "celltype")

set.seed(220705)

cn_1 <- kmeans(spe$aggregatedNeighbors, centers = 6)
spe$cn_celltypes <- as.factor(cn_1$cluster)

plotSpatial(spe, 
            node_color_by = "cn_celltypes", 
            img_id = "sample_id", 
            node_size_fix = 0.5) +
    scale_color_brewer(palette = "Set3")

mat <- colData(spe) %>% as_tibble() %>%
    group_by(cn_celltypes, celltype) %>%
    summarize(count = n()) %>%
    mutate(freq = count / sum(count)) %>%
    pivot_wider(id_cols = cn_celltypes, names_from = celltype, 
                values_from = freq, values_fill = 0) %>%
    ungroup() %>%
    select(-cn_celltypes)
## `summarise()` has grouped output by 'cn_celltypes'. You can override using the
## `.groups` argument.
pheatmap(mat, 
  color = colorRampPalette(c("dark blue", "white", "dark red"))(100), 
  scale = "column")

The step took 0.18 minutes.

spe <- buildSpatialGraph(spe, img_id = "sample_id", 
                         type = "knn", 
                         name = "knn_spatialcontext_graph", 
                         k = 40)

spe <- aggregateNeighbors(spe, 
                          colPairName = "knn_spatialcontext_graph",
                          aggregate_by = "metadata",
                          count_by = "cn_celltypes",
                          name = "aggregatedNeighborhood")

spe <- detectSpatialContext(spe, 
                            entry = "aggregatedNeighborhood",
                            threshold = 0.90,
                            name = "spatial_context")

spe <- filterSpatialContext(spe, 
                            entry = "spatial_context",
                            group_by = "patient_id", 
                            group_threshold = 3,
                            cells_threshold = 100,
                            name = "spatial_context_filtered")

plotSpatial(spe, 
            node_color_by = "spatial_context_filtered", 
            img_id = "sample_id", 
            node_size_fix = 0.5, 
            colPairName = "knn_spatialcontext_graph")

plotSpatialContext(spe,
                   entry = "spatial_context_filtered",
                   group_by = "sample_id",
                   node_color_by = "n_cells",
                   node_size_by = "n_group",
                   node_label_color_by = "n_cells") +
    scale_color_viridis()

The step took 0.31 minutes.

spe <- patchDetection(spe, 
                      patch_cells = spe$celltype == "Tumor",
                      img_id = "sample_id",
                      expand_by = 1,
                      min_patch_size = 10,
                      colPairName = "neighborhood")

plotSpatial(spe, 
            node_color_by = "patch_id", 
            img_id = "sample_id", 
            node_size_fix = 0.5) +
    theme(legend.position = "none") +
    scale_color_manual(values = rev(colors()))

The step took 0.41 minutes.

library(scales)

set.seed(220825)
out <- testInteractions(spe, 
                        group_by = "sample_id",
                        label = "celltype", 
                        colPairName = "neighborhood")

out %>% as_tibble() %>%
    group_by(from_label, to_label) %>%
    summarize(sum_sigval = sum(sigval, na.rm = TRUE)) %>%
    ggplot() +
        geom_tile(aes(from_label, to_label, fill = sum_sigval)) +
        scale_fill_gradient2(low = muted("blue"), mid = "white", 
  high = muted("red")) +
        theme(axis.text.x = element_text(angle = 45, hjust = 1))

The step took 7.09 minutes.

sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scales_1.2.1                pheatmap_1.0.12            
##  [3] igraph_1.3.4                viridis_0.6.2              
##  [5] viridisLite_0.4.1           caret_6.0-93               
##  [7] lattice_0.20-45             scran_1.24.0               
##  [9] BiocParallel_1.30.3         bluster_1.6.0              
## [11] batchelor_1.12.3            scater_1.24.0              
## [13] scuttle_1.6.3               patchwork_1.1.2            
## [15] dittoSeq_1.8.1              CATALYST_1.20.1            
## [17] cytomapper_1.9.1            EBImage_4.38.0             
## [19] forcats_0.5.2               stringr_1.4.1              
## [21] dplyr_1.0.10                purrr_0.3.4                
## [23] readr_2.1.2                 tidyr_1.2.1                
## [25] tibble_3.1.8                ggplot2_3.3.6              
## [27] tidyverse_1.3.2             openxlsx_4.2.5             
## [29] imcRtools_1.3.7             SpatialExperiment_1.6.1    
## [31] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.1
## [33] Biobase_2.56.0              GenomicRanges_1.48.0       
## [35] GenomeInfoDb_1.32.4         IRanges_2.30.1             
## [37] S4Vectors_0.34.0            BiocGenerics_0.42.0        
## [39] MatrixGenerics_1.8.1        matrixStats_0.62.0         
## [41] BiocStyle_2.24.0           
## 
## loaded via a namespace (and not attached):
##   [1] rsvd_1.0.5                  svglite_2.1.0              
##   [3] class_7.3-20                fftwtools_0.9-11           
##   [5] V8_4.2.1                    foreach_1.5.2              
##   [7] crayon_1.5.1                MASS_7.3-58.1              
##   [9] rhdf5filters_1.8.0          nlme_3.1-159               
##  [11] backports_1.4.1             reprex_2.0.2               
##  [13] rlang_1.0.5                 XVector_0.36.0             
##  [15] readxl_1.4.1                irlba_2.3.5                
##  [17] limma_3.52.2                rjson_0.2.21               
##  [19] bit64_4.0.5                 glue_1.6.2                 
##  [21] parallel_4.2.0              vipor_0.4.5                
##  [23] classInt_0.4-7              shinydashboard_0.7.2       
##  [25] haven_2.5.1                 tidyselect_1.1.2           
##  [27] distances_0.1.8             XML_3.99-0.10              
##  [29] zoo_1.8-10                  sf_1.0-8                   
##  [31] ggpubr_0.4.0                nnls_1.4                   
##  [33] xtable_1.8-4                magrittr_2.0.3             
##  [35] evaluate_0.16               cli_3.4.0                  
##  [37] zlibbioc_1.42.0             rstudioapi_0.14            
##  [39] sp_1.5-0                    rpart_4.1.16               
##  [41] bslib_0.4.0                 shiny_1.7.2                
##  [43] BiocSingular_1.12.0         xfun_0.32                  
##  [45] clue_0.3-61                 cluster_2.1.4              
##  [47] tidygraph_1.2.2             ggrepel_0.9.1              
##  [49] listenv_0.8.0               future_1.28.0              
##  [51] png_0.1-7                   ipred_0.9-13               
##  [53] withr_2.5.0                 bitops_1.0-7               
##  [55] aws.signature_0.6.0         ggforce_0.3.4              
##  [57] RBGL_1.72.0                 plyr_1.8.7                 
##  [59] cellranger_1.1.0            ncdfFlow_2.42.1            
##  [61] RTriangle_1.6-0.10          hardhat_1.2.0              
##  [63] pROC_1.18.0                 e1071_1.7-11               
##  [65] dqrng_0.3.0                 pillar_1.8.1               
##  [67] RcppParallel_5.1.5          GlobalOptions_0.1.2        
##  [69] cachem_1.0.6                multcomp_1.4-20            
##  [71] fs_1.5.2                    CytoML_2.8.1               
##  [73] raster_3.5-29               GetoptLong_1.0.5           
##  [75] DelayedMatrixStats_1.18.0   vctrs_0.4.1                
##  [77] ellipsis_0.3.2              generics_0.1.3             
##  [79] lava_1.6.10                 tools_4.2.0                
##  [81] beeswarm_0.4.0              munsell_0.5.0              
##  [83] tweenr_2.0.2                aws.s3_0.3.21              
##  [85] proxy_0.4-27                DelayedArray_0.22.0        
##  [87] fastmap_1.1.0               compiler_4.2.0             
##  [89] abind_1.4-5                 httpuv_1.6.6               
##  [91] prodlim_2019.11.13          GenomeInfoDbData_1.2.8     
##  [93] gridExtra_2.3               edgeR_3.38.4               
##  [95] ggnewscale_0.4.7            ggpointdensity_0.1.0       
##  [97] deldir_1.0-6                utf8_1.2.2                 
##  [99] later_1.3.0                 recipes_1.0.1              
## [101] jsonlite_1.8.0              concaveman_1.1.0           
## [103] graph_1.74.0                ScaledMatrix_1.4.0         
## [105] carData_3.0-5               sparseMatrixStats_1.8.0    
## [107] promises_1.2.0.1            car_3.1-0                  
## [109] doParallel_1.0.17           latticeExtra_0.6-30        
## [111] R.utils_2.12.0              rmarkdown_2.16             
## [113] sandwich_3.0-2              cowplot_1.1.1              
## [115] statmod_1.4.37              Rtsne_0.16                 
## [117] uwot_0.1.14                 HDF5Array_1.24.2           
## [119] survival_3.4-0              ResidualMatrix_1.6.1       
## [121] yaml_2.3.5                  plotrix_3.8-2              
## [123] systemfonts_1.0.4           cytolib_2.8.0              
## [125] flowWorkspace_4.8.0         htmltools_0.5.3            
## [127] locfit_1.5-9.6              graphlayouts_0.8.1         
## [129] digest_0.6.29               assertthat_0.2.1           
## [131] mime_0.12                   tiff_0.1-11                
## [133] units_0.8-0                 future.apply_1.9.1         
## [135] data.table_1.14.2           R.oo_1.25.0                
## [137] flowCore_2.8.0              drc_3.0-1                  
## [139] splines_4.2.0               labeling_0.4.2             
## [141] Rhdf5lib_1.18.2             googledrive_2.0.0          
## [143] RCurl_1.98-1.8              broom_1.0.1                
## [145] hms_1.1.2                   modelr_0.1.9               
## [147] rhdf5_2.40.0                colorspace_2.0-3           
## [149] DropletUtils_1.16.0         ConsensusClusterPlus_1.60.0
## [151] base64enc_0.1-3             BiocManager_1.30.18        
## [153] ggbeeswarm_0.6.0            shape_1.4.6                
## [155] nnet_7.3-17                 sass_0.4.2                 
## [157] Rcpp_1.0.9                  bookdown_0.28              
## [159] mvtnorm_1.1-3               circlize_0.4.15            
## [161] FlowSOM_2.4.0               RProtoBufLib_2.8.0         
## [163] fansi_1.0.3                 tzdb_0.3.0                 
## [165] ModelMetrics_1.2.2.2        parallelly_1.32.1          
## [167] R6_2.5.1                    grid_4.2.0                 
## [169] ggridges_0.5.3              lifecycle_1.0.2            
## [171] zip_2.2.1                   curl_4.3.2                 
## [173] ggsignif_0.6.3              googlesheets4_1.0.1        
## [175] jquerylib_0.1.4             Matrix_1.4-1               
## [177] RcppAnnoy_0.0.19            TH.data_1.1-1              
## [179] RColorBrewer_1.1-3          iterators_1.0.14           
## [181] gower_1.0.0                 svgPanZoom_0.3.4           
## [183] htmlwidgets_1.5.4           beachmat_2.12.0            
## [185] polyclip_1.10-0             terra_1.6-7                
## [187] rvest_1.0.3                 ComplexHeatmap_2.12.1      
## [189] globals_0.16.1              codetools_0.2-18           
## [191] lubridate_1.8.0             randomForest_4.7-1.1       
## [193] metapod_1.4.0               gtools_3.9.3               
## [195] dbplyr_2.2.1                R.methodsS3_1.8.2          
## [197] gtable_0.3.1                DBI_1.1.3                  
## [199] httr_1.4.4                  highr_0.9                  
## [201] KernSmooth_2.23-20          stringi_1.7.8              
## [203] vroom_1.5.7                 reshape2_1.4.4             
## [205] farver_2.1.1                hexbin_1.28.2              
## [207] Rgraphviz_2.40.0            timeDate_4021.104          
## [209] magick_2.7.3                DT_0.24                    
## [211] xml2_1.3.3                  colorRamps_2.3.1           
## [213] ggcyto_1.24.1               BiocNeighbors_1.14.0       
## [215] interp_1.1-3                scattermore_0.8            
## [217] bit_4.0.4                   jpeg_0.1-9                 
## [219] ggraph_2.0.6                pkgconfig_2.0.3            
## [221] gargle_1.2.1                rstatix_0.7.0              
## [223] knitr_1.40